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Record W4310079148 · doi:10.1002/cjce.24776

Modelling of catalytic reactors containing core–shell pellets with various morphologies for series reactions

2022· article· en· W4310079148 on OpenAlexvenueno aff
Young‐Sang Cho

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThiele modulusPelletsCatalysisDiffusionParticle (ecology)ChemistryReaction rateChemical engineeringVolume (thermodynamics)InertMaterials scienceThermodynamicsDispersion (optics)Thermal diffusivityComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Modelling of series reactions was performed for core–shell catalysts. Mathematical solutions of concentrations inside the pellets were derived from reaction–diffusion equations considering inert‐core thickness ( ξ c ) for first‐order kinetics. Transient behaviours of catalytic reactors containing core–shell pellets were predicted, assuming pseudo‐steady state approximation. In a batch reactor, the removal rate of reactants increased with increasing Thiele modulus and decreasing ξ c in the order of sphere > cylinder > slab. The transient concentration of the intermediate product was maximum and affected by the distribution coefficient, diffusivity ratio, particle shape, and ξ c . In a continuously stirred tank reactor, the concentration was affected by feed rate and catalyst loading, and conversion could be enhanced by a cascade connection. In a fixed‐bed reactor, the concentration increased with increasing ξ c due to an insufficient catalyst volume. Péclet number and particle shape also affected the concentration, implying that axial dispersion and interfacial area are important design parameters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.191
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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